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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Methods to allow pandas.DataFrame."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import uuid
import numpy as np
from tensorflow.python.estimator.inputs.queues import feeding_functions
from tensorflow.python.util.tf_export import estimator_export
try:
# pylint: disable=g-import-not-at-top
# pylint: disable=unused-import
import pandas as pd
HAS_PANDAS = True
except IOError:
# Pandas writes a temporary file during import. If it fails, don't use pandas.
HAS_PANDAS = False
except ImportError:
HAS_PANDAS = False
def _get_unique_target_key(features, target_column_name):
"""Returns a key that does not exist in the input DataFrame `features`.
Args:
features: DataFrame
target_column_name: Name of the target column as a `str`
Returns:
A unique key that can be used to insert the target into
features.
"""
if target_column_name in features:
target_column_name += '_' + str(uuid.uuid4())
return target_column_name
@estimator_export('estimator.inputs.pandas_input_fn')
def pandas_input_fn(x,
y=None,
batch_size=128,
num_epochs=1,
shuffle=None,
queue_capacity=1000,
num_threads=1,
target_column='target'):
"""Returns input function that would feed Pandas DataFrame into the model.
Note: `y`'s index must match `x`'s index.
Args:
x: pandas `DataFrame` object.
y: pandas `Series` object or `DataFrame`. `None` if absent.
batch_size: int, size of batches to return.
num_epochs: int, number of epochs to iterate over data. If not `None`,
read attempts that would exceed this value will raise `OutOfRangeError`.
shuffle: bool, whether to read the records in random order.
queue_capacity: int, size of the read queue. If `None`, it will be set
roughly to the size of `x`.
num_threads: Integer, number of threads used for reading and enqueueing. In
order to have predicted and repeatable order of reading and enqueueing,
such as in prediction and evaluation mode, `num_threads` should be 1.
target_column: str, name to give the target column `y`. This parameter
is not used when `y` is a `DataFrame`.
Returns:
Function, that has signature of ()->(dict of `features`, `target`)
Raises:
ValueError: if `x` already contains a column with the same name as `y`, or
if the indexes of `x` and `y` don't match.
ValueError: if 'shuffle' is not provided or a bool.
"""
if not HAS_PANDAS:
raise TypeError(
'pandas_input_fn should not be called without pandas installed')
if not isinstance(shuffle, bool):
raise ValueError('shuffle must be provided and explicitly set as boolean '
'(it is recommended to set it as True for training); '
'got {}'.format(shuffle))
if not isinstance(target_column, six.string_types):
raise TypeError('target_column must be a string type')
x = x.copy()
if y is not None:
if target_column in x:
raise ValueError(
'Cannot use name %s for target column: DataFrame already has a '
'column with that name: %s' % (target_column, x.columns))
if not np.array_equal(x.index, y.index):
raise ValueError('Index for x and y are mismatched.\nIndex for x: %s\n'
'Index for y: %s\n' % (x.index, y.index))
if isinstance(y, pd.DataFrame):
y_columns = [(column, _get_unique_target_key(x, column))
for column in list(y)]
target_column = [v for _, v in y_columns]
x[target_column] = y
else:
x[target_column] = y
# TODO(mdan): These are memory copies. We probably don't need 4x slack space.
# The sizes below are consistent with what I've seen elsewhere.
if queue_capacity is None:
if shuffle:
queue_capacity = 4 * len(x)
else:
queue_capacity = len(x)
min_after_dequeue = max(queue_capacity / 4, 1)
def input_fn():
"""Pandas input function."""
queue = feeding_functions._enqueue_data( # pylint: disable=protected-access
x,
queue_capacity,
shuffle=shuffle,
min_after_dequeue=min_after_dequeue,
num_threads=num_threads,
enqueue_size=batch_size,
num_epochs=num_epochs)
if num_epochs is None:
features = queue.dequeue_many(batch_size)
else:
features = queue.dequeue_up_to(batch_size)
assert len(features) == len(x.columns) + 1, ('Features should have one '
'extra element for the index.')
features = features[1:]
features = dict(zip(list(x.columns), features))
if y is not None:
if isinstance(target_column, list):
keys = [k for k, _ in y_columns]
values = [features.pop(column) for column in target_column]
target = {k: v for k, v in zip(keys, values)}
else:
target = features.pop(target_column)
return features, target
return features
return input_fn
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